Crisscross boustrophedonic flight patterns for UAV scanning and imaging

ABSTRACT

An unmanned autonomous vehicle assessment and reporting system may implement a boustrophedonic flight pattern for capturing images of a structure to develop a three-dimensional model of the same. A crisscross boustrophedonic flight pattern may include two or more boustrophedonic flight patterns that are at angles relative to one another.

RELATED APPLICATIONS

This application is a continuation of Non-Provisional application Ser.No. 16/035,888 filed on Jul. 16, 2018 titled “Crisscross BoustrophedonicFlight Patterns for UAV Scanning and Imaging,” which claims priority toProvisional Application No. 62/576,640 filed on Oct. 24, 2017, titled“Crisscross Boustrophedonic Flight Patterns for UAV Scanning andImaging,” both of which are hereby incorporated by reference in theirentireties.

TECHNICAL FIELD

This disclosure generally relates to systems and methods for autonomousanalyses, inspections, reporting, and remediation estimates forstructures and other property. Specifically, this disclosure relates toreal-time surface and subsurface analyses using autonomous vehicles(such as analyses of roof surfaces and/or subsurfaces), associatedreporting systems, and associated visualization systems.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the disclosure aredescribed herein, including various embodiments of the disclosure withreference to the figures listed below.

FIG. 1A illustrates an example of a user interface for initiating one ofthree scan types, including a crisscross scan, a test square scan, and adetail drop scan via an electronic computing device.

FIG. 1B illustrates an example of a user interface for initiating adefault UAV roof analysis via an electronic computing device, where thedefault scan may be any one or more of the three scan types of FIG. 1A.

FIG. 2A illustrates boustrophedonic flight path of UAV for assessing aproperty, according to one embodiment.

FIG. 2B illustrates second boustrophedonic flight path of UAV that,together with the flight pattern of FIG. 2A, forms a crisscross flightpattern for assessing a property, according to one embodiment.

FIG. 2C illustrates an acute angle boustrophedonic flight pattern 282that is at an angle other than 90-degrees relative to the firstboustrophedonic flight pattern 280 in FIG. 2A.

FIG. 3 illustrates a single-flight crisscross flight pattern of a UAVfor assessing a property, according to one embodiment.

FIG. 4A illustrates a first boustrophedonic flight pattern with roundedstructure-facing end passes to capture obliques during boustrophedonicscanning, according to one embodiment.

FIG. 4B illustrates a second boustrophedonic flight pattern with roundedstructure-facing end passes that, together with the flight pattern ofFIG. 4A, forms a crisscross flight pattern for assessing property withintegrated oblique image capture, according to one embodiment.

FIG. 4C illustrates a single-flight crisscross flight pattern of a UAVfor assessing a property with integrated oblique image capture viarounded structure-facing end passes, according to one embodiment.

FIG. 4D illustrates an example of a single-flight crisscrossboustrophedonic flight pattern of a UAV for capturing scan data via oneor more sensors, according to one embodiment.

FIG. 5A illustrates a first boustrophedonic flight pattern of acrisscross flight pattern showing example camera angles for integratedoblique image capture during the crisscross flight pattern, according toone embodiment.

FIG. 5B illustrates a boustrophedonic flight pattern that includesbackward flying during a withdraw portion of passes over a structure tocapture additional oblique images of the structure, according to oneembodiment.

FIG. 5C illustrates a boustrophedonic flight pattern that includestilting a camera to a rearward position during portions of each passover a structure to capture additional obliques images of the structure,according to one embodiment.

FIG. 6 illustrates a first boustrophedonic flight pattern of acrisscross flight pattern showing example camera angles for integratedoblique image capture during the crisscross flight pattern using roundedstructure-facing end passes, according to one embodiment.

FIG. 7 illustrates an example of the user interface of FIG. 1A in whichthe test square scan is selected via the electronic computing device.

FIG. 8 illustrates an example of a user interface for initiating acustomized UAV roof analysis from an electronic computing device,according to one embodiment.

FIG. 9A illustrates a UAV determining a pitch of a roof, according toone embodiment.

FIG. 9B illustrates a UAV determining a pitch of a roof, according to analternative embodiment.

FIG. 10 illustrates a three-dimensional model of a roof displayed on anelectronic computing device produced using imaging data collected by aUAV, including one or more patch scan analyses, according to variousembodiments.

FIG. 11 illustrates a close-up view of a patch scan analysis on anelectronic computing device, according to one embodiment.

FIG. 12 illustrates close-up views of patch scan analyses for each faceof a roof on an electronic computing device, according to oneembodiment.

FIG. 13 illustrates a three-dimensional rendering of a house withannotated damage markers and patch scan region outlines, according tovarious embodiments.

FIG. 14 illustrates a UAV using the date and time to identify and/oreliminate shadows in image captures, according to one embodiment.

FIG. 15 illustrates a roof-type analysis result displayed on anelectronic computing device, according to one embodiment.

FIG. 16 illustrates an estimate of repairs based on patch scan analysesand a roof type analysis presented on an electronic computing device,according to various embodiments.

FIG. 17 illustrates a block diagram of a UAV roof analysis system foranalyzing a roof and presenting the results of the analysis, accordingto one embodiment.

FIG. 18 illustrates a system for roof analysis including a library ofdata profiles for computer vision matching, according to one embodiment.

FIG. 19 illustrates examples of possible library images of dataprofiles, according to one embodiment.

DETAILED DESCRIPTION

Roof damage assessment and remediation estimates generally require ahuman assessor to scale a ladder to examine a roof. The assessor mayattempt to follow a set of guidelines during the assessment in anattempt to provide systematic and uniform reports and estimates. It iswidely recognized that there is some subjectivity to these types ofanalyses and difficulty in producing uniform and objective results. Forthis reason, it is not uncommon to find that an agent of a roof repaircompany may report that there is more damage to a roof than an insuranceadjuster might report. Uniformity in the assessment is difficult due tohuman biases and subjective analysis techniques.

The assessor may also have a difficult time showing an owner, or anotherinterested person, what exactly is wrong with a roof. Photographs takenwith handheld cameras from atop the roof are often devoid of context andlack sufficient detail to convey convincing or understandable evidenceof damage, repair plans, etc., to an owner or another interested party.For example, a homeowner may find a photograph of a group of shinglesdevoid of the context necessary to draw their own conclusions.Similarly, a soft spot caused by water damage photographed using ahandheld camera from on top of the roof may not convey sufficientlymeaningful information to a party of interest because it lacks thecontext of the surrounding roof.

Moreover, the number and types of people that can act as agents,assessors, adjusters, etc. are limited because these individuals must beable to scale a ladder and perform a roof analysis. In many instances,this limits the number and types of people that can act as assessorsand/or agents for owners, insurance companies, roofing companies,government analysts, and others that may have an interest in evaluatinga roof. For example, while a person may be a good salesperson, insuranceagent, or adjuster, if they are unwilling or incapable of scaling aladder and walking around on a roof, they may not be able to perform thenecessary tasks.

This disclosure provides methods and systems for assessing damage,defects, anomalies, identifying materials, determining variouscharacteristics, and/or generally capturing images or other scan data onroofs of residential, commercial, and industrial buildings, including,but not limited to, single-family homes, condominiums, townhomes, officebuildings, industrial buildings, sheds, storage units, and otherstructures with roofs on them. The systems and methods described hereinfor assessing damage on a roof include the use of autonomouslycontrolled unmanned aerial vehicles (UAVs) to ensure that roof analysesare systematic and uniform. This disclosure also provides systems andmethods for three-dimensional modeling, visualizing damage assessments(e.g., via patch scans of sample regions), determining roofingmaterials, and producing systematic and uniform remediation estimates.

A UAV may carry an imaging system to capture multiple images of theroof. The UAV may capture visible light images, infrared images, and/orultraviolet images. Other sensor types may be used as well, including,but not limited to moisture sensors, ultrasonic sensors, LIDAR, RADAR,etc. False color representations may be generated for visualizing sensordata from non-visible spectrum image sensors. Thus, the term “image” isused broadly herein to include visible-spectrum images as well as‘images’ captured using alternative sensor types, such as infrared,ultraviolet, and ultrasonic sensor systems.

The systems and methods described herein may be implemented by an ownerof the roof or an agent of a company (including a representative,contractor, or employee thereof). Examples of companies that mightutilize the systems and methods described herein include, but are notlimited to, an insurance company, a roofing company, a damage assessmentcompany, an inspector, a government analyst, an appraiser, or otherproperty valuation, evaluation, assessment, or repair company.

In various embodiments, an agent may function as an operator of the UAVand utilize a remote-control system, such as a personal computer orpersonal electronic device. Examples of such devices include watches,tablets, laptops, smart glasses, wearable tech, and mobile phones. Anoperator may use the computing device to initiate an assessment via asoftware program. In some embodiments, the agent may use the softwareprogram to select a standard or default assessment, three-dimensionalmodeling, specific detail scans, etc. A settings menu may be used beforeor during a flight as well. For example, settings for a patch scan maybe selected for a desired sample size (e.g., sample region dimensionsfor a patch scan analysis), and/or a desired scope of the assessment. Insome embodiments, the operator may begin the UAV assessment by placingthe UAV in a safe location for takeoff and selecting a “start” or“begin” icon on the computing device.

The UAV may be programmed to perform an analysis of the nearest roof orone or more roofs based on a selection by the operator. For instance,the operator may use satellite images or real-time nadir images from theUAV to select one or more structures having one or more roofs for theUAV to analyze.

In some embodiments, the UAV may initially position itself above theselected roof(s) to capture nadir images of the roof(s) and/orassociated structures. In some embodiments, the nadir image may be usedto align the UAV and/or scan data (e.g., captured images) with dataavailable from another source (e.g., satellite images associated withGPS data). Examples of GPS offset alignment are described in U.S.Provisional Patent Application No. 62/501,326 titled “GPS OffsetCalibration for UAVs” filed on May 15, 2017, which application is herebyincorporated by reference in its entirety. Additional examples ofapproaches for GPS offset alignment are described in U.S. patentapplication Ser. No. CURRENTLY UNKNOWN also titled “GPS OffsetCalibration for UAVs,” which application claims priority to theabove-identified provisional application and is also hereby incorporatedby reference in its entirety.

The UAV may follow a boustrophedonic flight path or flight pattern whilethe imaging system captures a series of images and/or collectsnon-visible image scan information. In some embodiments, the UAV mayalso position itself at various altitudes and angles relative to theroof to collect oblique images at one or more heights and/or relative toeach face of the roof. To facilitate rendering of a three-dimensionalmodel, the UAV may perform a loop scan of the roof while the imagingsystem captures a set of oblique images. For additional detailed imagesof the roof, the UAV and imaging system may perform a series of microscans, sometimes referred to as detailed micro scans or microscans. Amicro scan may consist of or include a patch scan analysis of a patch orsample region with defined dimensions. Using the collection of images, arendering system may generate interactive models of the roof and/oroptionally the underlying structure. Examples of patch scans, such astest square samples, are described in U.S. patent application Ser. No.15/444,164 filed on Feb. 27, 2017, titled “Systems and Methods forSurface and Subsurface Damage Assessments, Patch Scans, andVisualization,” which application is hereby incorporated by reference inits entirety.

In some embodiments, a loop scan may be omitted and a crisscrossboustrophedonic flight pattern may be utilized that captures obliquesduring approach portions of each pass of the crisscross boustrophedonicflight pattern and/or during rounded structure-facing end passes. Asdescribed below, a crisscross boustrophedonic flight pattern may includetwo boustrophedonic flight patterns that are performed at an anglerelative to one another (e.g., at a 90-degree angle). In someembodiments, a crisscross boustrophedonic flight pattern may includemore than two boustrophedonic flight patterns, each of which is at anangle to the others (e.g., three crisscross boustrophedonic flightpatterns at 120-degree angles relative to one another). Regardless ofthe number of boustrophedonic flight patterns used (1, 2, 3, . . .etc.), the camera may be transitioned to various angles during one ormore passes, as described below, to allow for the generation of athree-dimensional model without the use of a distinct loop scan and/orseparate oblique image captures.

A camera (or another sensor) of a UAV may be tilted up to face orpartially face the structure as the UAV approaches the structure. Thecamera may begin to tilt downward as the UAV gets closer to thestructure during each pass, such that when the UAV begins to pass overthe structure the camera is pointed downward. The camera may remainpointe downward as the UAV passes the structure and begins to turn forthe next pass of a given boustrophedonic flight pattern.

As the UAV is again approaching the structure (this time from the otherside), the camera may again be pointed toward the structure to captureoblique images. As the UAV again begins to pass over the structure, thecamera may point downward for the remainder of that pass. Thus, duringeach pass of a given boustrophedonic flight pattern, the camera may bepointed toward the structure during each approach and transition (e.g.,gradually) to a downward orientation (tilt angle) while it passes overthe structure and during the departure from the structure (i.e., beforethe UAV turns around for the next pass of the boustrophedonic flightpattern).

In some embodiments, one or both of the end passes of one or more of theboustrophedonic flight patterns of a crisscross boustrophedonic flightpattern may comprise structure-facing end passes. A structure-facing endpass may be rounded in some embodiments. Regardless of whether thestructure-facing end pass(es) are rounded or not, the UAV may be rotated(e.g., flown sideways) to capture a series of images with the UAV facingthe structure as the “end pass” (e.g., first and/or last pass) of givenboustrophedonic scan. In some embodiments, the structure-facing endpasses, including rounded structure-facing end passes, may be used incombination with the forward-angled camera orientation image captureapproach during approaches of the other passes (i.e. non-end passes) ofeach boustrophedonic scan.

In some embodiments, the slope of the roof may be determined during theboustrophedonic flight pattern(s) and/or patch scans may be conductedduring one or more passes of the boustrophedonic flight pattern(s). Insome embodiments, the UAV may interrupt the pass of the boustrophedonicflight pattern to determine the slope and/or capture patch scan data.This may allow for an integrated flight pattern where all data iscaptured during a single flight pattern that appears to be a crisscrossboustrophedonic flight pattern with interruptions for one or more of:patch scans, shadow avoidance, slope determinations, detailedmicroscans, etc.

The UAV may, optionally, capture a nadir image (i.e., top-down) of theentire site. The UAV roof analysis system may use the nadir image toalign the UAV with landmarks established in the initial identificationof the site, structure, and/or roof. The UAV roof analysis system mayalso use the nadir image to generate a flight pattern or adjust apredefined flight pattern to ensure accuracy and uniformity. The flightpattern may include any combination of one or more flight patterns,including: (1) a crisscross boustrophedonic scan, (2) a patch scan, and(3) user selected micro scans. In some embodiments, a roof analysis mayrequire only one or two of the three types of scans. Thus, in someembodiments, one or more scan types may be omitted. For instance, insome situations a single crisscross boustrophedonic scan may be used togenerate a three-dimensional model of a structure and include integratedpatch scans and/or detailed micro scans of the structure. In otherembodiments, the crisscross boustrophedonic scan may be used to generatea three-dimensional model, while distinct scans are used for patch scansand/or detailed microscans.

In one implementation, an operator may manually navigate a UAV to alocation proximate a face of a roof. Autonomously, or in response to anoperator request, the UAV roof analysis system may conduct a patch scananalysis of the roof face. In some embodiments, the UAV system maydirect the operator to (1) navigate the UAV up, down, left, right,forward, or backward and/or (2) change the angle of a sensor system(e.g., camera) on the UAV to facilitate a patch scan analysis. Inanother embodiment, once the UAV has been positioned proximate a face ofa roof by an operator, a “perform patch analysis” button may be selectedand the UAV roof analysis system may autonomously navigate the UAV tothe correct location by making minor positional adjustments and/oradjust sensor systems as needed to perform one or more patch scananalyses on the face of the roof. In still other embodiments, asdescribed herein, the entire processes from takeoff to landing may beautomated and patch scan analysis(es) may be conducted for one or morefaces of the roof of a structure.

In embodiments utilizing a crisscross boustrophedonic scan, the UAV mayfollow a flight pattern where the UAV travels from edge to edge of thesite or roof edges in alternating offset zones (or slightly beyond eachedge of a roof or slightly less than each edge of a roof). The camera oranother sensing system on the UAV may capture images of the roof as theUAV travels in the boustrophedon pattern. The UAV roof analysis systemmay merge the images to form a detailed aerial view of the roof and/orunderlying structure and site. The level of detail in the detailedaerial view may be improved by lowering the altitude of the UAV andusing minimal offsets. However, the altitude used for a boustrophedonicscan may be limited due to the height of structures and obstacles on thesite. In some embodiments, varying altitudes may be utilized to duringapproaches, departures, during end passes, during various non-endpasses, etc.

In some embodiments, the boustrophedonic scan alone may be used todevelop a top-down or aerial view of the roof. In other embodiments, theimages and scan information obtained during the boustrophedonic scan maybe combined with other available data or used to refine other availabledata. The scan information may, as previously described, includeinformation from optical imaging systems, ultrasonic systems, radar,LIDAR, infrared imaging, moisture sensors, and/or other sensor systems.

During a third scan stage, the UAV may perform a micro scan for close-upand/or otherwise detailed data capture of the roof using one or moresensors and/or sensor types. For examples, tens, hundreds, thousands, oreven millions of pixels of sensor data may be used to capture eachsquare inch of a roof or other surface or subsurface. The level ofdetail far exceeds that available via other types of aerial imaging fora given sensor system. The micro scan may include patch scans of one ormore faces of the roof. The micro scan of the roof may provide detailedimages (visible spectrum or otherwise) for analyzing the roof. Thegranularity from the micro scan may assist in detailed measurements,damage identification, and material identification. For example, themicro scan may allow an insurance adjuster to zoom in on athree-dimensional model of the structure to view and assess a patch ofroof with a predetermined size and/or shape. In some embodiments,detailed images (or other scan data) captured during the crisscrossboustrophedonic scan may constitute detailed micro scans and/or providedata for patch scans.

A patch scan may comprise an analysis of a region or sample section of aroof having a predetermined square footage, size, shape, and/or relativelocation. The patch scan analysis may identify damage, assess theseverity of the damage, identify colors, materials, etc. An assessmentof the severity of the damage is not subjective. Rather, the severity ofthe damage may be categorized based on material type and be objectivelyassociated with a loss of life expectancy, reduced structural integrity,water permeability, loss in insulation qualities, loss of reflectivequalities, and/or an objective loss of aesthetic appeal (e.g., apercentage of pixels mismatched as compared to an undamaged portion ofthe roof). Thus, in a basic embodiment the size of a damage point alonemay be used to assign a severity value to the damage point.

In one embodiment, to perform the patch scans, the UAV may perform aseries of vertical descents toward the rooftop or alternativelyhorizontal approaches to the rooftop. A patch scan may be performedseparately from the crisscross boustrophedonic scan, or during thecrisscross boustrophedonic scan. For instance, the UAV may begin in astarting position at the base altitude (or horizontal distance) andlower its altitude (or decrease its horizontal distance) until it is ata target distance from the rooftop. In one embodiment, the camera oranother sensor system on the UAV may capture an image when the targetdistance is reached. In another embodiment, the camera may take a set ofimages as the UAV approaches the rooftop. After the image at the targetdistance is captured, the UAV may return to the further distance andtravel a target lateral distance and once again approach that roof to atarget distance. In some embodiments, the images may slightly overlap toensure coverage of the entire structure. In other embodiments, a singlepatch scan (or another predefined number of patch scans) from each faceof the roof may be sufficient. The UAV may continue to performapproaches separated by a defined lateral distance until the entirerooftop has been assessed or until the desired number of patch scans perface of the roof has been completed.

While compatibility with industry standard patch sizes may be useful insome embodiments, in other embodiments it may be preferred to conduct adetailed analysis of the entire structure or entire roof. Moreover,computer vision techniques, computer learning algorithms, and/orartificial intelligence techniques may be employed in combination withone or more of the embodiments described herein. For example, in someembodiments, computer vision (CV) techniques may be employed to detectdamage of both interior and exterior surfaces and sub-surfaces of astructure. Examples of these techniques include, but are not limited to,pattern recognition, edge detection, measurements, ratio analysis, etc.

Thus, a traditional patch analysis requires a human adjuster to inspecta roof and draw sample region using, for example, chalk. For example,the adjuster may draw a sample region that is 10′×10′ or 15′×6.666′ fora 100 square-foot sample region. The dimensions of the square orrectangular sample region may be increased or decreased for a targetsquare footage and or specific length or width. A sample region isgenerally used to reduce the workload of the adjuster or anotherevaluator. The concept is that the sample region is large enough to berepresentative of the remainder of the roof, and so there is no need todo a complete analysis of the entire roof.

However, this can lead to inaccurate conclusions, incorrect repairs,and/or time and money being spent for naught. Using the systems andmethods described herein, real-time adaptive analysis of an entire roofor entire face of a roof may be performed. For instance, the systems andmethods described herein may be used to automatically detect damage,such as hail, over the entire surface of the roof thereby eliminatingthe need for a manual inspection process. Nevertheless, to conform toindustry standards and/or to attain a sufficiently accurate analysis fora give application, patch scans such as test squares and/or testrectangles with sample regions having defined dimensions and/or squarefootage targets may be utilized as described herein.

In various embodiments, each patch scan may be performed with imagesensors orthogonal to the rooftop at a center of the patch scan. Forinstance, a 10′×10′ patch scan may comprise positioning the UAV apredefined distance from the surface of the roof at a center point ofthe 10′×10′ patch with the sensor(s) orthogonal to the patch of theroof. As noted herein, the “test square” may be rectangular and mayinclude a defined number of square feet. For example, a 100 square-footsample size may be used that is captured by a rectangle that is 15′ by6.666′.

In another embodiment, to perform the micro scan, the UAV may traversethe rooftop at a target lateral distance, and sensors may capture imagesand other sensor data as the UAV travels in a boustrophedonic. To avoida collision, the UAV may use integrated sensors and/or data capturedduring a prior loop scan or boustrophedonic scan.

In various embodiments, UAV hardware, firmware, and/or software may bemodified, upgraded, and/or programmed to perform the functions, methods,and behaviors described herein. In some embodiments, software, hardware,and/or firmware may be created to interface with pre-existing UAVinterfaces. Such hardware and software may be integrated into the UAV,into a portable computing device used by the agent (or homeowner), or becloud-based and accessible to one or both of the UAV and the portablecomputing device.

In some embodiments, modifications to one or more portions of a UAV maybe made to accomplish the described systems and methods. Hardware,firmware, and/or software may also be used in conjunction with a UAV toextend or replace its capabilities to implement any of the embodimentsdescribed herein. In some embodiments, multiple UAVs may be utilizedthat together provide the desired feature set for a particularapplication. For example, one UAV may be used for infrared scanning anda different UAV may be used for visible image captures. In someembodiments, the same UAV may be used, but the operator may swap sensorsystems during various portions of the scan (e.g., halfway through ascan, an operator may remove a visible spectrum camera and replace itwith an infrared camera).

While many of the examples described herein relate to damage assessmentsand roof replacement repair estimates, similar technology and approachescould be used with minor adaptations for use by rooftop installers, suchas satellite dish installers, solar panel installers, swamp coolerinstallers, antenna installers, and the like.

In some embodiments of the present disclosure, a technician may manuallyoperate a UAV to navigate it around the structure while the UAVautonomously captures the needed data for a desired assessment. In manyembodiments, the use of a UAV facilitates and/or augments the servicesprovided by a human, it does not necessarily replace the human role. Forexample, usage of the systems and methods described herein allow aninsurance company or roofing company to send an agent skilled incustomer relations and/or sales, regardless of whether that person hastraining in roof damage analysis or roofing estimates. In otherembodiments, a human operator may not be required because the UAV mayautonomously perform the one or more of the operations described herein.

A UAV roof analysis system, according to various embodiments describedherein, provides a comprehensive, automatic (or at leastsemi-automatic), and methodical approach for assessing damage on a roofand/or for providing an estimate for remediation of the roof. The typesof assessments, reports, and images collected may vary based on aspecific application. Generally, the approaches obviate the need for anindustry-specific trained technician to be present or at least greatlyreduce the workload of such a technician. In some embodiments, thesystems and methods described herein may change the qualifications thatdefined a “technician” qualified to perform analysis of a structure.

The UAV roof analysis system may include a site selection interface toreceive an electronic input identifying a location of a roof or roofs.The UAV roof analysis system may also include a hazard selectioninterface to receive electronic input identifying geographic hazardssuch as aboveground power lines, tall trees, neighboring structures,etc. In various embodiments, the UAV assessment and reporting system maybe preloaded geographic hazard models. The UAV roof analysis system mayallow for these hazards to be eliminated from the flight plan to producea safe path for automated imagery and data capture. The selection of theroof(s) and/or hazards may be performed through an interface on theagent's computing device using satellite images, in real-time based onimages transmitted by the UAV, and/or on a previously captured nadirimage of a site. Onboard sensors for obstacle avoidance may additionallyor alternatively be used for the detection of hazardous obstacles,especially in situations in which incomplete geographic information isavailable and periodic changes are expected.

As previously noted, the UAV may include a visible spectrum camera tocapture images of the structure, sonar sensors, LIDAR sensors, infraredsensors, optical sensors, radar sensors, and the like. The UAV mayinclude one or more onboard processors and/or communication interfacesto communicate with a controller, the computing device, and/or acloud-based software program. The UAV and/or the agent's computingdevice may include a non-transitory computer-readable medium forreceiving and storing instructions that, when executed by the processor,cause the UAV to conduct a roof analysis, as described herein. The roofanalysis may include a crisscross boustrophedonic scan of the roof. Eachboustrophedonic scan of the crisscross boustrophedonic scan may includecapturing images during a boustrophedonic flight pattern within a firstaltitude range or first range of altitudes.

In various embodiments, a roof selection interface on the agent'scomputing device may receive, from the operator/agent, an electronicinput identifying a roof. The operator may mark, via an electronic inputon a roof identification interface, one or more boundaries associatedwith the roof, structure, and/or site. The operator may also identify,on the operator client, obstacles, boundaries, structures, andparticular points of interest.

For example, an operator who is attempting to scan a residential lot maybe presented with a satellite image on a tablet. The operator may selecteach corner of the lot to identify the boundaries of the lot. Theoperator may additionally or alternatively drag a finger or stylus alongthe outline of roof, or faces of each roof section, to mark theperimeter of the roof or roof faces. Further, if the lot has trees orother obstacles, the operator may, for example, press and hold toidentify their location and enter an estimated height. The operator mayalso emphasize certain portions or faces of the roof for analysis, forenhanced analysis, or to be excluded from analysis. For instance, if theoperator is collecting data for an insurance claim on a house that isknown to have experienced potentially damaging hail from a northwestdirection, the operator may highlight the north-facing and west-facingsurfaces of the roof for analysis.

A UAV may begin an analysis of a roof with a defined scanning plan toevenly scan a roof or section of a roof. During a defined or dynamicflight pattern, a UAV may detect damage through the use of artificialintelligence (AI), computer vision analysis techniques, and/or throughlibrary-matching techniques as described herein. The detected damage maybe analyzed according to a ruleset and result in the UAV altering thetypes of scanning being performed, the level of detail being collected,and/or modify or alter a flight path in real time. Accordingly,real-time modifications to a scanning or navigation pattern may allowfor more accurate and/or enhanced (e.g., more detailed) scan data to becollected on an as-needed basis.

In some embodiments, the UAV roof analysis system may automaticallyidentify obstacles, boundaries, structures, and particular points ofinterest using satellite images, county records, topographical maps,and/or customer statements. For example, the UAV roof analysis systemmay receive an address of a commercial property to be assessed fordamage caused by a tornado. The UAV roof analysis system may useavailable county records to determine the boundary of the property andlocation of the roof-bearing structure(s) thereon, and topographicalmaps of the area to identify objects and structures.

In some embodiments, a UAV may utilize artificial intelligence, computervision techniques, and/or computer learning algorithms to optimize aflight plan and navigate safes during each flight based on real-timescanning and sensor data. Each subsequent flight or scanning session maybe used to update a knowledge base of hazards and other features of aproperty or flight pattern.

In one embodiment, the UAV may include proximity sensors. The proximitysensors may be used to avoid obstacles on and surrounding the roof andthereby identify safe flight areas above and proximate the roof andsurrounding objects. The proximity sensors may also be used to determinehow close the UAV is to the structure. For example, a UAV may beprogrammed to capture images at a distance of five feet from thestructure. The proximity sensors may send a signal indicating to the UAVthat it has reached the target distance, five feet, and the camera maycapture sensor data in response to the signal. The target distance maybe adjusted based on desired detail, weather conditions, surfaceobstacles, camera resolution, camera field of view, and/or sensorattributes. In some embodiments, infrared and other non-optical sensorsmay be used to provide additional assessment data. For example,materials may be identified based on a spectral analysis and/or damagemay be identified based on infrared leaks in a structure.

In other embodiments, the UAV may use additional and/or alternativemethods to detect proximity to obstacles and the structure. For example,the UAV may use topographical data. As another example, the UAV may havea sonar system that it uses to detect proximity.

Additionally, in some embodiments, the UAV roof analysis system mayperform multiple micro scans with different levels of resolution and/orperspective. For example, a first micro scan with patch analysis mayprovide detailed images at 10 or 20 feet above a roof. Then a secondmicro scan with patch analysis may image a portion of the roof at fivefeet for additional detail of that section. This may allow a fastercapture of the roof overall while providing a more detailed image set ofa portion of interest. In one embodiment, the UAV roof analysis systemmay use the first micro scan to determine the portion to be imaged inthe second micro scan.

In some embodiments, the UAV roof analysis system may use each portionof a scan to improve the next portion of a scan. For example, the firstportion of a scan may identify the location of objects. Sonar or opticalsensors may be used in the first portion of a scan to identify theheight of the objects and/or physical damage.

During crisscross boustrophedonic flight patterns and/or as part ofpatch scan analyses, the UAV roof analysis system may automaticallycalculate a pitch of a roof. In a first embodiment, the UAV roofanalysis system may use the UAV's sonar or object detection sensors tocalculate the pitch of the roof. For example, the UAV may begin at anedge of the roof and then travel toward the peak. The pitch may then becalculated based on the perceived Doppler effect as the roof becomesincreasingly closer to the UAV as it travels at a constant verticalheight. In a second embodiment, the UAV may land on the roof and use apositioning sensor, such as a gyroscope, to determine the UAV'sorientation. The UAV roof analysis system may use the orientation of theUAV to determine the slope.

In some embodiments, a UAV may hover above the roof but below a peak ofthe roof. Sensors may determine a vertical distance to the roof belowand a horizontal distance to the roof, such that the roof represents thehypotenuse of a right triangle with the UAV positioned at the 90-degreecorner of the right triangle. A pitch of the roof may be determinedbased on the rise (vertical distance down to the roof) divided by therun (horizontal forward distance to the roof).

In some embodiments, a UAV may hover above the roof at a first locationand measure a vertical distance from the UAV to the roof (e.g.,downward). In one such embodiment, a downward sensor may be used. TheUAV may then move horizontally to a second location above the roof andmeasure the vertical distance from the UAV to the roof. Again, the roofbecomes the hypotenuse of a right triangle, with one side of thetriangle corresponding to the horizontal difference between the firstlocation and the second location, and the second side of the trianglecorresponding to the vertical difference between the distance from theUAV to the roof in the first location and the distance from the UAV tothe roof in the second location.

In some embodiments, a UAV may hover above the roof at a first locationand measure a horizontal distance from the UAV to the roof. In suchembodiments, a forward-, lateral-, and/or reverse-facing sensor(s) maybe used. The UAV may then move vertically to a second location above theroof and measure the horizontal distance from the UAV to the roof.Again, the roof becomes the hypotenuse of a right triangle, with oneside of the triangle corresponding to the vertical difference betweenthe first location and the second location, and the second side of thetriangle corresponding to the horizontal difference between the distancefrom the UAV to the roof in the first location and the distance from theUAV to the roof in the second location.

In some embodiments, the UAV roof analysis system may use three or moreimages and metadata associated with those images to calculate the pitchof the roof. For example, the UAV may capture a first image near theroof. The UAV may then increase its altitude and capture a second imageabove the first image. The UAV may then fly laterally towards the peakof the roof until the proximity of the UAV to the roof is the same asthe proximity of the first image. The UAV may then capture a thirdimage. Each image may have metadata associated with it including GPScoordinates, altitude, and proximity to the house. The UAV roof analysissystem may calculate the distance of the roof traveled based on the GPScoordinates and altitude associated with the three images using thePythagorean theorem. The UAV roof analysis system may then calculate thepitch by taking the ratio of the altitude and the distance of the rooftraveled.

In some embodiments, to maintain stationary a UAV may have to tilt thebody and/or one or more propellers to compensate for wind or otherenvironmental factors. For various measurements and scans describedherein, the images, measurements, and/or other captured data may beannotated to identify the tilt or angle caused by the UAV tilt. In otherembodiments, the sensors, cameras, and other data capture tools may bemechanically or digitally adjusted, such as gyroscopically, for example.In some embodiments, measurements, such as distances when calculatingskew and/or roof pitch, may be adjusted during calculations based onidentified UAV tilt due to environmental factors. Similar measurementsfrom various points relative to the roof may be used to identify saggingor bulging portions of the roof that fit within a patch size or arelarger than a patch size.

The UAV may use the calculated pitch to adjust the angle of the camerato reduce image skew during a micro scan and/or loop scan. For example,once the pitch is calculated the UAV may perform a micro scan with thecamera at a perpendicular angle to the roof and/or de-skew the imageusing software on the UAV, during post-imaging processing, and/orthrough cloud-based processing. In various embodiments, the calculatedpitch is used to angle the camera so it is perpendicular (orthogonal) tothe roof to eliminate skew during patch scan analyses.

In some embodiments, a pitch determination system may determine a pitchof the roof based on at least two distance measurements, as describedabove, that allow for a calculation of the pitch. An imaging system ofthe UAV may capture an image of the roof of the structure with theoptical axis of the camera aligned perpendicular to a plane of the roofof the structure by adjusting a location of the UAV relative to a planarsurface of the roof and/or a tilt angle of the camera of the UAV.

The UAV roof analysis system may also reduce and/or identify shadows inthe images by calculating the current angle of the sun. The UAV roofanalysis system may calculate the angle of the sun based on the time ofthe day, the day of the year, and GPS location. To eliminate the UAV'sshadow from appearing in captured images, the UAV roof analysis systemmay apply the angle of the sun to the current UAV position in flight.The UAV position, the angle/position of the sun, and the relativelocation of surfaces and structures (e.g., roof) may determine preciselywhere the shadow of the UAV will appear. The UAV may adjust its positionand camera based on the location of the roof shadow to ensure that eachphotograph will be captured in such a way as to substantially orcompletely eliminate the UAV's shadow.

In some embodiments, the UAV roof analysis system may also use the angleof the sun to determine the best time of day to photograph a site orportion of a site. For example, the shadow of an object on a site mayobscure a structure during the morning. Based on the angle of the sun,the UAV roof analysis system may determine what time of day the shadowwould no longer obscure the structure. The UAV may autonomously collectimages during different times of day to ensure that shadow-free imagesof all, most, or specific portions of the structure are captured duringboustrophedonic, loop, and/or micro scans. The systems and methodsdescribed herein are repeatable on a consistent basis for variousproperties and structures and are therefore aptly characterized assystematic.

In other embodiments, a shadow determination system (local or remote)may calculate (as opposed to directly observe) a location of a shadowcast by the proximate object onto the structure based on a currentlocation of the sun, which can be accurately determined based on acurrent time and a GPS location of the structure. The imaging system mayaccount for the shadow by (1) annotating images of the structure thatinclude the calculated shadow, (2) adjusting an exposure of images ofthe structure that include the calculated shadow, and/or (3) identifyinga subsequent time to return to the structure to capture non-shadowedimages of the portions of the structure that are currently shadowed.

The UAV, server, and operator client may be connected via one or morenetworks. For example, the UAV may transmit images to the server via acellular network. Additionally, the UAV may connect to the client via asecond network such as a local wireless network. The UAV, server, andoperator client may each be directly connected to each other, or one ofthe elements may act as a gateway and pass information received from afirst element to a second element.

A standard flight plan may be saved on the server. The standard flightplan may be loaded on the UAV and altered based on information enteredby the operator into the operator client interface. The UAV (e.g., viaonboard or cloud-based processors) may also alter the standard flightplan based on the images captured and/or other sensor data.

A UAV system may include onboard processing, onboard storage,communications systems, access to cloud-based processing, and/or accessto cloud-based storage. The system may utilize one or more of theseresources to analyze, image, and/or otherwise scan the roof. In someembodiments, the system may utilize computer vision in combination witha library of images for identifying properties, characteristics ofproperties, problems, defects, damage, unexpected issues, and the like.

The inclusion of computer vision intelligence may be adapted based onthe use of computer vision in other fields and in its general form foruse in UAV roof analysis. Computer visional analysis may include varioussystems and methods for acquiring, processing, analyzing, storing, andunderstanding captured images. The system may include digital and analogcomponents, many of which may be interchangeable between analog anddigital components. Computer vision tasks may be performed in the cloudor through onboard processing and storage. The computer vision system ofthe UAV may execute the extraction of high-dimensional data fromcaptured images (optical, infrared, and/or ultraviolet) and other sensordata to produce numerical or symbolic information.

The computer vision systems may extract high-dimensional data to makedecisions based on rule sets. High-dimensional data is more than merethree-dimensional image capture (i.e., more than simple stereoscopicimaging). Rather, high-dimensional (or multi-dimensional data) comprisesdata for which the number of measured/recorded parameters associatedwith other measured/recorded parameters is many. Using image recognitiontechniques, for example, a set of n images may each have a resolution ofm pixels by k pixels. We can view each pixel within the image as avariable so that each of the n images resides in an m×k dimensionalspace. From there a training set of images may be used to recognize newdefects, structures, material types, etc. Depending on the applicationand the images, the training/new images may be presented in a lowerdimensional sub-space.

Using extracted high-dimensional data (i.e., multi-dimensional data),computer vision techniques may be used for a rule-based analysis ofstructures that is systematic, uniform, and repeatable. The computervision systems may utilize images, video sequences, multi-dimensionaldata, time-stamped data, and/or other types of data captured by any of awide variety of electromagnetic radiation sensors, ultrasonic sensors,moisture sensors, radioactive decay sensors, and/or the like.

Part of the analysis may include profile matching by comparing capturedsensor data with data sets from a library of identifiable sensorprofiles. An evaluator module or system may be responsible or partiallyresponsible for this analysis. Such an analysis may be performed locallyand/or in the cloud. For example, images of different types of shingles(e.g., asphalt, cedar, and clay) may be used to determine which type ofshingle is on a roof being analyzed. Upon a determination that theshingles are asphalt, the system may compare captured images of theasphalt shingles on the roof with a library of defects in asphaltshingles to identify matching defects. The system may also use computervision analysis techniques, artificial intelligence decision-makingtechniques, optionally in combination with a library of data to modifyalter the flight plan or path in real-time based on materials and/ordamage that are detected.

For example, during a scanning process, one or more sensors may collectinformation that may be used to query a rule set. The rule set maymodify a navigation pattern, flight direction, scan type, scan details,or other action to be taken or being taken by the UAV in response to arule set's interpretation of the collected information.

As another example, a thermal scan of asphalt shingles may reveal athermal profile data set that can be compared with a library of thermalprofiles. A matched profile may be used to determine that the roof isundamaged, damaged, aging, poorly constructed, etc. In some embodiments,a first sensor system may be used and, if a matched profile is found,the system may follow a rule set to take a subsequent action that isdifferent from the action that would have been taken if no matchedprofile had been found. An evaluator system or module (hardware,firmware, or software) may evaluate various inputs to make a decisionand/or determine that human operator input is required.

In one example embodiment, an optical scan may be used to match profileswithin the library that indicate that a portion of the roof may have aparticular characteristic (e.g., damage, manufacturing material,construction material, construction methods, modification from the priorspecification, etc.). A rule set may dictate that, based on the matchedprofile within the library, another type of sensor system should be usedfor a subsequent scan and/or indicate that a scan with increasedresolution or detail is warranted. In some embodiments, athree-dimensional representation of the roof may be visualized on acomputing device. The agent, homeowner, or another user may click on alocation on the three-dimensional representation to view micro scans,such as a patch scan.

As above, numerous examples and descriptions are given with respect toroof surfaces and roof subsurfaces. Roofs are merely one example of asurface or subsurface that can be analyzed and/or scanned using thesystems and methods described herein. Many, if not all, of theembodiments and combinations of embodiments of the systems and methodsdescribed herein may be applied to various exterior and interiorsurfaces of a structure or other property.

The term “surface or subsurface” as used herein is used in an inclusivesense such that scanning or analyzing “a surface or subsurface” mayinclude scanning or scanning or analyzing the surface, the subsurface,or both the surface and the subsurface. A surface may include anyexposed surface of a structure or other property. A subsurface mayinclude anything beneath, behind, hidden, or obscured by the surface toone or more sensor types.

For instance, an optical imaging sensor may be used to scan a surface ofa roof. An infrared imaging sensor may be used to scan a surface of aroof as well, but may also be used to image thermal variations in thesubsurface of the structure, such as embedded moisture, underlyingstructural members, and the like.

Some of the infrastructure that can be used with embodiments disclosedherein is already available, such as: general-purpose computers,computer programming tools and techniques, digital storage media, andcommunications networks. A computer may include a processor, such as amicroprocessor, microcontroller, logic circuitry, or the like. Theprocessor may include a special-purpose processing device, such as anASIC, a PAL, a PLA, a PLD, a CPLD, a Field Programmable Gate Array(FPGA), or other customized or programmable device. The computer mayalso include a computer-readable storage device, such as non-volatilememory, static RAM, dynamic RAM, ROM, CD-ROM, disk, tape, magneticmemory, optical memory, flash memory, or another computer-readablestorage medium.

Suitable networks for configuration and/or use, as described herein,include any of a wide variety of network infrastructures. Specifically,a network may incorporate landlines, wireless communication, opticalconnections, various modulators, demodulators, small form-factorpluggable (SFP) transceivers, routers, hubs, switches, and/or othernetworking equipment.

The network may include communications or networking software, such assoftware available from Novell, Microsoft, Artisoft, and other vendors,and may operate using TCP/IP, SPX, IPX, SONET, and other protocols overtwisted pair, coaxial, or optical fiber cables, telephone lines,satellites, microwave relays, modulated AC power lines, physical mediatransfer, wireless radio links, and/or other data transmission “wires.”The network may encompass smaller networks and/or be connectable toother networks through a gateway or similar mechanism.

Aspects of certain embodiments described herein may be implemented assoftware modules or components. As used herein, a software module orcomponent may include any type of computer instruction orcomputer-executable code located within or on a computer-readablestorage medium, such as a non-transitory computer-readable medium. Asoftware module may, for instance, comprise one or more physical orlogical blocks of computer instructions, which may be organized as aroutine, program, object, component, data structure, etc., that performone or more tasks or implement particular data types, algorithms, and/ormethods.

A particular software module may comprise disparate instructions storedin different locations of a computer-readable storage medium, whichtogether implement the described functionality of the module. Indeed, amodule may comprise a single instruction or many instructions, and maybe distributed over several different code segments, among differentprograms, and across several computer-readable storage media. Someembodiments may be practiced in a distributed computing environmentwhere tasks are performed by a remote processing device linked through acommunications network. In a distributed computing environment, softwaremodules may be located in local and/or remote computer-readable storagemedia. In addition, data being tied or rendered together in a databaserecord may be resident in the same computer-readable storage medium, oracross several computer-readable storage media, and may be linkedtogether in fields of a record in a database across a network.

Some of the embodiments of the disclosure can be understood by referenceto the drawings, wherein like parts are designated by like numeralsthroughout. The components of the disclosed embodiments, as generallydescribed and illustrated in the figures herein, could be arranged anddesigned in a wide variety of different configurations. Further, thoseof skill in the art will recognize that one or more of the specificdetails may be omitted, or other methods, components, or materials maybe used. In some cases, operations are not shown or described in detail.Thus, the following detailed description of the embodiments of thesystems and methods of the disclosure is not intended to limit the scopeof the disclosure, as claimed, but is merely representative of possibleembodiments.

FIG. 1A illustrates an example of a user interface 100 for initiatingone of three scan types, including a crisscross scan 110, a test squarescan 111, and a detail drop scan 112 via an electronic computing device.The user interface 100 may be implemented on a computing device, such asa tablet, mobile phone laptop, etc. In other embodiments, the userinterface 100 may be implemented on a dedicated controller of a UAV. Instill other embodiments, the user interface 100 may be implemented on ascreen (e.g., a touch screen) directly on a UAV. Various settings forthe scan may be available via a settings interface 120. In someembodiments, a full scan 130 may be selected that will automaticallyimplement one or more of the crisscross scan 110, the test square scan111, and the detail drop scan 112. In some embodiments, the test squarescan 111 may be integrated as part of the crisscross scan 110 and notavailable as a distinct option.

FIG. 1B illustrates an example of an alternative user interface 150 forinitiating a standard or default UAV roof analysis, via icon 160, froman electronic computing device, according to one embodiment. Asillustrated, the user interface 150 may allow an operator (e.g., anowner or agent, representative, contractor, or employee of a company) topush a button (e.g., touch, click, key entry, etc.) to begin analysis.In various embodiments, the system may collect data from the entire roofand/or a predefined sample size from one or more sections of the roof.In various embodiments, the entire roof may be scanned and analyzed. Toconform with various industry standards, the entire roof may be scannedusing one or more patch scan regions with defined dimensions. Again,settings 120 may allow for the default flight and/or analysis to bedefined and/or adjusted for a particular application.

FIG. 2A illustrates a boustrophedonic scan of a rooftop defined by theidentified geographic boundaries 250 that include a structure 220.During the boustrophedonic scan, the UAV 275 may capture images whilefollowing a boustrophedonic flight pattern 280. For clarity, the numberof passes shown is eight; however, the actual number of passes may varybased the size of the roof being analyzed, elevation of the UAV, desireddetail level, sensor field of view (e.g., camera zoom), desired overlapof captured images, etc. Similarly, the flight path may not extend tothe boundaries of the property but instead only extend just beyond orjust short of the boundaries of the roof of the structure 220.

Alternatively, the flight path may extend beyond the boundaries of theproperty. In some embodiments, the boundaries 250 of the property areirrelevant and/or not provided. In such instances, the flight path maybe adapted to capture sufficient images or other scan data of thestructure 220. Again, the number of passes, length of the passes, andthe distance between the passes may depend on a desired resolution,camera field of view, camera resolution, height of the UAV 275 relativeto the roof, and/or other characteristics of the desired scan,capabilities of the UAV 275, and attributes of the surface.

The UAV 275 may fly to a start location or be launched from the startlocation. The start location may be at a first corner of the property250 or the roof of the structure 220. The UAV 275 may then follow astraight path until a boundary line or edge of the roof is reached. TheUAV 275 may then turn and follow an offset path in the oppositedirection. While rounded turns are illustrated, in some embodiments theturns may have sharp corners instead since the UAVs can rotate in theair without forward or lateral movement. The UAV 275 may continue totravel back and forth until an endpoint 285 is reached and the entireroof (or a selected portion of the roof) has been traveled. The UAV 275may travel at a high altitude such that it will not collide with anyobstacle or structure and/or avoid obstacles in the path by going aroundor above them. During the flight, the UAV 275 may capture images. Insome embodiments, onboard processing or cloud-based processing may beused to identify structures and obstacles. Alternatively, the system mayperform an analysis after scanning is complete and the UAV has returnedhome.

In some embodiments, a loop scan may be used to take a series of angledimages of the roof (commonly referred to as obliques) to aid in thecreation of a digital three-dimensional model. Various approachesdiscussed herein obviate the need for “obliques” and/or the loop scanaltogether. Thus, the scanning techniques and flight patterns describedherein may improve the speed at which a scan can be completed. The useof fewer images to build a three-dimensional model may decrease theprocessing time, decrease the amount of data stored, decrease processingand interpolation errors, decrease the time to perform image capture,etc.

The orientation of the boustrophedonic flight pattern 280 is definedgenerally in the direction of the arrows shown on the parallel sectionsof each pass of the boustrophedonic flight pattern. In the illustratedembodiment, the transition from one pass to a neighboring (i.e., next orprevious) pass is made via a rounded pass-offset portion. The width ofeach pass-offset defines the spacing between passes. In some embodiments(not illustrated) the ends of the passes may be connected via a squaredpass-offset portion. In still other embodiments, the passes may beconnected via an angled pass-offset portion (see, e.g., FIG. 4D).

FIG. 2B illustrates second boustrophedonic flight pattern 281 that is atan angle relative to the first boustrophedonic flight pattern 280 inFIG. 2A. Together with the boustrophedonic flight pattern 280 of FIG.2A, the second boustrophedonic flight pattern 281 forms a crisscrossflight pattern for assessing a property, according to variousembodiments. The first and second flight patterns 280 and 281 may bedistinct flight patterns with different endpoints 285 and 286 anddifferent starting points of the UAV 275. In other embodiments, a singlecrisscross boustrophedonic flight pattern that includes twoboustrophedonic flight patterns may be implemented.

FIG. 2C illustrates an acute angle boustrophedonic flight pattern 282that is at an angle other than 90-degrees relative to the firstboustrophedonic flight pattern 280 in FIG. 2A. Together with theboustrophedonic flight pattern 280 of FIG. 2A and/or the boustrophedonicflight pattern 281 of FIG. 2B, the acute angle boustrophedonic flightpattern 282 forms a crisscross flight pattern for assessing a property,according to various embodiments. The first, second, and/or acute flightpatterns 280, 281, 282 may be distinct flight patterns with differentendpoints 285, 286, 287 and different starting points of the UAV 275. Inother embodiments, a single crisscross boustrophedonic flight patternthat includes two or three boustrophedonic flight patterns (280, 281,and/or 282) may be implemented.

FIG. 3 illustrates a single-flight crisscross flight pattern 380 of aUAV 375 for scanning a structure 320, according to one embodiment. Afirst boustrophedonic flight pattern of the single-flight crisscrossflight pattern 380 is shown in solid lines and includes locations 1-6. Asecond boustrophedonic flight pattern of the single-flight crisscrossflight pattern 380 is shown in dashed lines and includes locations 6-11.The illustrated embodiment shows the crisscross flight pattern 380including two distinct boustrophedonic flight patterns (dashed and solidlines) at 90-degree angles relative to one another. Moreover, theillustrated embodiment shows the boustrophedonic flight patterns (dashedand solid lines) at substantially perpendicular angles relative to theexterior walls of the structure 320.

In some embodiments, the crisscross flight pattern 380 may include morethan two distinct boustrophedonic flight patterns (e.g., 3 or 4) at90-degree angles relative to one another. In some embodiments, thesingle-flight crisscross flight pattern 380 may include two or moredistinct boustrophedonic flight patterns at angles that are more than orless than 90 degrees relative to one another. For example, threedistinct boustrophedonic flight patterns may be flown that are at 120degrees relative to one another. In some embodiments, one or more of thetwo or more boustrophedonic flight patterns may be intentionally flownat angles relative to the exterior walls of the structure 320 to captureimproved obliques of the structure. In some embodiments, a nadir imagecaptured by the UAV 375, plat maps, architectural drawings, and/or asatellite image of the structure 320 may be used to orient the UAV 375and/or the two or more boustrophedonic flight patterns of the crisscrossflight pattern 380.

In various embodiments, the crisscross boustrophedonic flight patterncomprises two or more boustrophedonic flight patterns at an anglerelative to one another and is sufficient to capture all the necessaryscan data (e.g., images, infrared data, etc.) for: (i) capturing a nadirimage of a structure; (ii) generating a three-dimensional model of astructure; (iii) capturing “oblique” images of the structure; (iv)generating patch scans with detailed analysis of defects, damage, and/orother anomalies (e.g., via test squares or test rectangles); and/or (v)capturing microscans of one or more portions of the structure or othersubject.

FIG. 4A illustrates a first boustrophedonic flight pattern 481 that mayoptionally be part of a crisscross flight pattern (as shown in FIG. 4C)with rounded structure-facing end passes 490 and 491 to capture obliquesduring boustrophedonic scanning of a structure 420, according to oneembodiment. As illustrated, a UAV 475 may be angled toward a structureduring an initial end pass 490. The UAV 475 may be rotated to face thestructure 420 while it travels in the arc or curved end pass 490 tocapture obliques. In some embodiments, the UAV 475 may travel the arc orcurved end pass 490 by moving laterally and backward as it rotatesclockwise, and then laterally and forward as it rotates clockwise tomaintain the UAV facing the structure 320.

The UAV may then complete the other passes (solid lines) of theboustrophedonic flight pattern 481 before it begins to travel the arc orcurved end pass 491. The elevation of the end passes 490 and 491 may bedifferent or the same as the other passes (solid lines) of theboustrophedonic flight pattern 481. The UAV may complete the firstboustrophedonic flight pattern 481 at the location 499 and land there orat another designated landing location. Alternatively, the UAV mayimmediately begin a second (or third, fourth, etc.) boustrophedonicflight pattern at the location 499 or at another designated startlocation of the second boustrophedonic flight pattern.

FIG. 4B illustrates a second boustrophedonic flight pattern 482 withrounded structure-facing end passes 492 and 493 that, together with theflight pattern 481 of FIG. 4A, forms a crisscross flight pattern forassessing the structure 420 with integrated oblique image capture duringthe rounded structure-facing end passes 490-493, according to variousembodiments.

FIG. 4C illustrates a single-flight crisscross flight pattern of a UAV475 that includes the first and second boustrophedonic flight patterns481 and 482 from FIGS. 4A and 4B. As illustrated, the single-flightcrisscross flight pattern begins at the marked location of UAV 475 witha rounded structure-facing end pass 490 and then completes passes (solidlines) of the first boustrophedonic flight pattern 481 and roundedstructure-facing end pass 491. Rounded structure-facing end pass 491transitions into rounded structure-facing end pass 493, followed bypasses (dashed lines) of the second boustrophedonic flight pattern 482,and concludes with rounded structure-facing end pass 492 to end atlocation 497. The UAV may land at location 497 or any other designatedlocation proximate or distant from the structure 420.

In some embodiments, the rounded structure-facing end passes 490-493 maybe modified to fit a specific curve and/or to be non-rounded (i.e., astraight end pass in which the UAV 475 is structure-facing or,alternatively, not structure-facing). As previously described, asingle-flight or multi-flight crisscross flight pattern may include anynumber of boustrophedonic flight patterns that may or may not includestructure-facing end passes and/or rounded structure-facing end passes.

FIG. 4D illustrates an example of a single-flight crisscrossboustrophedonic flight pattern 450 of a UAV for capturing scan data viaone or more sensors, according to one embodiment. The illustratedembodiment includes two boustrophedonic flight patterns that are at a90-degree angle relative to one another to form the crisscrossboustrophedonic flight pattern 450. A first boustrophedonic flightpattern may begin at point A and follow points B, C, and D, and end atpoint E. The UAV may then return to point F and begin a secondboustrophedonic flight pattern that includes points F, G, and H, endingat I.

As illustrated, each pass of the boustrophedonic flight pattern mayinclude a segment that is at an angle relative to another segment of thesame pass. For instance, the pass between points B and C includes asegment from B to W that is at an angle relative to the segment from Wto C. Though not required, the second pass of the first boustrophedonicflight pattern from point C to point D includes a segment from Y to Zthat is substantially parallel to the segment between W and X of thefirst pass between points B and C. In some embodiments, the angledsegments (e.g., pass-offset portions B to W and C to Y) may be used tocapture structure-facing images in which the UAV is rotated toward thestructure during image capture such that the UAV is traveling forwardand sideways along the segment.

Each pass may include an approach portion (e.g., approximately,pass-offset portion BW), a flyover portion (e.g., approximately, WX),and a departure portion (e.g., approximately, XC). In other embodiments,as illustrated herein, the pass-offset portions may be rounded andfather from the structure, in which case the approach portion may be atthe same orientation as the flyover portion and/or the departure portion(see, e.g., FIG. 3).

A nadir image of the structure 421 can be captured during the crisscrossboustrophedonic flight pattern 450. A camera or other sensor device onthe UAV may be angled upward toward the structure during each approachportion of the crisscross boustrophedonic flight pattern 450 to capturestructure-facing images until the UAV begins to pass over, just beforethe UAV begins to pass over, or just after the UAV begins to pass overthe structure 421 (i.e., proximate a location where the UAV begins topass over the structure 421).

The illustrated embodiment has been implemented and demonstrated toenable the capture of scan data sufficient to: generate athree-dimensional model of the structure, capture nadir images of thestructure and/or proximate land 405, capture oblique images of thestructure, capture structure-facing images; capture high-resolution scandata sufficient for microscans; determine pitch(es) of one or moreportions of the roof of the structure; generate patch scans of sampleregions of one or more faces of the roof of the structure; identifyconstruction defects, installation defects, material defects,compromised portions of the structure, moisture damage, and/or otheranomalies in the surface or subsurface of the structure; identifymaterials, and/or determine other characteristics of the structure.

In various embodiments, the crisscross boustrophedonic flight pattern450 may include more or fewer passes in one or both boustrophedonicflight patterns. The spacing between passes of one or bothboustrophedonic flight patterns may be wider or closer together.Moreover, the spacing between passes of one of the boustrophedonicflight patterns may be different than the spacing between passes of theother boustrophedonic flight pattern.

FIG. 5A illustrates a first boustrophedonic flight pattern of acrisscross flight pattern showing example camera angles for integratedoblique image capture of a structure 520 during the crisscross flightpattern, according to one embodiment. As illustrated, a camera of theUAV 575 may be angled upward (toward the structure 520) as it approachesthe structure 520 and transition to a downward angle as it passes overthe structure 520 until it turns around. The transition of the camerafrom a forward or near-forward facing angle to the downward ornear-downward facing angle may be gradual as it approaches, such that itis angle more upward/forward when it is farther from the structure 520.Alternatively, it may transition from a preset forward/upward angle to apreset downward angle at a predefined location relative to the structure520.

In the illustrated embodiment, the dashed arrows and the camerasillustrated on the undersides of the UAVs 575 are angled during aninitial portion 580 of the first pass of the boustrophedonic flightpattern (on approach) and then transition to downward once they reach asecond portion 581 of the first pass of the boustrophedonic flightpattern (above the structure and on departure). Similarly, the cameramay again be angled toward the structure 520 on the approach portion 582of the second pass of the boustrophedonic flight pattern and transitionto a downward direction on the portion 583 of the second pass that isabove the structure 520. The camera may remain angled downward duringthe departure portion 584 of the second pass after the structure 520.Again, any number of passes may be used for a boustrophedonic scan andmultiple boustrophedonic scans may be used to form a crisscrossboustrophedonic scans.

In various embodiments, the camera of the UAV may have limitedrotational and/or tilt capabilities. For example, the camera (e.g., themount or gimble) may allow for tilt between approximately 0 degrees(straight down) and 90 degrees (parallel to forward motion). The cameramay not be able to rotate to the left or right at all. In otherembodiments, the camera (or, again, an associated mount or gimble) mayhave a tilt range of approximately 120 degrees. As described above, thecamera may be tilted toward the structure 520 on approach and thengenerally downward for the remainder of the pass since it cannot betilted toward the structure (i.e., in the opposite direction of travel).

FIG. 5B illustrates an alternative embodiment in which the camera of theUAV 575 is angled upward (toward the structure 520) as it approaches, at590, the structure 520. During the approach 590, the camera transitionsto a downward angle as it approaches a middle portion, at 591, of thestructure 520. Rather than the camera remaining tilted to the downwardangle for the remainder of the pass (as in FIG. 5A), the UAV 575 rotates180 degrees, at 599, and travels backward for the remainder of the pass(i.e., during a “withdraw” portion of the pass).

As the UAV 575 travels backward, the camera tilts upward gradually toremain angled toward the structure as the UAV 575 retreats or withdrawsbackward from the structure 520 for the remainder of the pass, at 592. Aprotruding antenna 576 has been added to the rear of the UAV 575 toprovide a visual indication of the direction of travel during eachportion of each pass along the boustrophedonic flight pattern. Inpractice, such an antenna my not be visible and is simply added to thedrawings to provide directional context. It is appreciated that any of awide variety of UAV designs, shapes, sizes, etc. may utilize, or bemodified to utilize, the systems and methods described herein.

The transition of the camera from the downward position to the rearwardfacing angle as the UAV 575 retreats, at 592, may be gradual so that thecamera is angled more upward/rearward when it is farther from thestructure 520. Alternatively, it may transition from a presetrearward/upward angle to a preset downward angle at a predefinedlocation relative to the structure 520. The same pattern of cameratilting and backward flying can be performed for each pass of theboustrophedonic flight pattern to capture images of the structure 520 atvarious angles during a continuous boustrophedonic flight pattern or, asdescribed herein, during a continuous flight that includes two or moreboustrophedonic flight patterns at angles relative to one another.

FIG. 5C illustrates an alternative embodiment in which the camera of theUAV can tilt to a rearward facing position. As illustrated, the cameraof the UAV 575 is angled upward (toward the structure 520) as itapproaches, at 560, the structure 520. As it approaches, the cameratransitions to a downward angle as it passes over the structure (e.g.,as it passes over a middle of the structure or as it begins to pass overa portion of the structure). As the UAV 575 passes the structure 520,the camera is tilted to a rearward facing angle to continue to captureimages of the structure at various angles as the UAV gets farther awayfrom the structure.

In some embodiments, the camera may also be able to rotate left andright to allow angled images of the structure 520 during flight passesthat are not over the top of the structure 520. The same pattern ofcamera tilting (and/or rotating) can be performed for each pass of theboustrophedonic flight pattern to capture images of the structure 520 atvarious angles during a continuous boustrophedonic flight pattern, or acombination of multiple boustrophedonic flight patterns at anglesrelative to one another as described herein.

FIG. 6 illustrates a first boustrophedonic flight pattern of acrisscross flight pattern showing example camera angles of a UAV 675 forintegrated oblique image capture during the crisscross flight patternusing rounded structure-facing end passes, according to one embodiment.As illustrated, a camera may be rotated on the UAV 675 during first andsecond rounded structure-facing end passes 690 and 691, respectively. Inother embodiments, the camera may not be able to rotate or rotation ofthe UAV may be undesirable due to, for example, decreased visibilityfrom landing gear of the UAV. In such instances, the UAV 675 may rotateand the camera may be angled upward toward the structure 620. The UAV675 may then travel sideways (strafing) and backward/forwards tocomplete the rounded structure-facing end passes 690 and 691 of theboustrophedonic flight pattern. As further illustrated, during each ofthe other, non-end passes of the UAV 676, the camera may be angledforward/upward toward the structure on approach and then transition todownward once it is over the structure 675 and until the end of eachpass (as described in conjunction with FIG. 5).

FIG. 7 illustrates an example of the user interface 100 of FIG. 1A inwhich the test square scan 111 is selected via the electronic computingdevice. In some embodiments, as described in conjunction with FIG. 1B,the test square scan 111 may be integral to or performed during thecrisscross model scan 110.

FIG. 8 illustrates an example of a user interface 800 for initiating aUAV roof analysis from an electronic computing device, according to oneembodiment. As illustrated, a user interface may allow an operator(e.g., an owner or agent, representative, contractor, or employee of acompany) to select a sample size for patch scans and begin a UAV flightand analysis. For example, the operator may select a height 810 and/orwidth 820 setting. In some embodiments, the setting may be a totalsquare footage setting instead of a height/width. In other embodiments,the sample dimensions may be a fixed aspect ratio, such that only onesetting need be input (e.g., a square footage, a height, or a width.

In various embodiments, the sample size selection may correspond to apatch scan region with defined dimensions that are accepted by anindustry standard. While the illustrated embodiment allows for arectangular selection based on width and height, a wide variety ofalternatives are possible. For instance, in one embodiment the selectionmay simply be a number “patches” per face of the roof, where each patchconforms to a standard size. In another embodiment, no selection may beavailable at all as the UAV may simply perform a standardized patchscan. Alternatively, each face of the roof may be considered its ownpatch region with unique dimensions.

As an example, the International Association of Certified HomeInspectors requires that a 10′×10′ square section be used when possiblewith corners marked in chalk and damage points circled within the100-square-foot region. Accordingly, to conform with the standard, inone embodiment the UAV system may digitally mark (e.g., overlaymarkings) on a 10′×10′ section of the roof and digitally annotate (e.g.,overlay markings on) each damage point therein.

FIG. 9A illustrates a UAV determining a pitch 921 of a roof 920 of astructure. The UAV 975 may capture three or more images of the roof: afirst image at a first horizontal displacement location 975, a secondimage at a second horizontal displacement location 976, and a thirdimage at a third horizontal displacement location 977. The UAV may usethese images along with associated metadata, including proximity data,to determine the pitch 921 of the roof.

The UAV may also detect inconsistencies 930, such as a depression orbulge, in the shingles on the roof. The inconsistencies 930 may be asign of damage to the roof. The UAV may mark the inconsistency 930 as aportion of interest for a subsequent patch scan analysis.

In various embodiments, the UAV 975 includes a propulsion system to movethe UAV 975 from a first aerial location to a second aerial locationrelative to a structure. Movements may be horizontal, vertical, and/or acombination thereof. Lateral movements and rotation may also bepossible. As previously described, the UAV may include one or moresensors that can be used, or possibly are specifically configured todetermine distances to objects, such as the roof 920. The UAV maydetermine a distance to a roof at a first aerial location. The UAV maythen move to a second aerial location along a movement vector thatincludes one or more directional components (e.g., up, down, left,right, back, or forward, which could be more generally described asvertical, horizontal, or lateral, or even described using an X, Y, and Zcoordinate system). A distance to the roof may be calculated at thesecond aerial location. A pitch of the roof may be calculated (e.g.,geometrically) based on the distance measurements at the first andsecond locations and at least one of the components of the movementvector.

FIG. 9B illustrates a UAV 975 in a stationary location with forward anddownward distance measurement sensors used to determine the pitch of theroof from a single location. Additional examples of pitch calculationsand uses thereof are described in U.S. patent application Ser. No.15/710,221 filed on Sep. 20, 2017, titled “Systems and Methods forAutonomous Perpendicular Imaging of Test Squares,” which application ishereby incorporated by reference in its entirety.

FIG. 10 illustrates a three-dimension model 1020 that may be developedusing data gathered during one or more scans, such as theboustrophedonic scans shown in any one or more of FIGS. 2A-6.Specifically, the three-dimensional model 1020 may be created usingimages gathered during a crisscross boustrophedonic scan with roundedstructure-facing end passes, as illustrated in FIG. 4C and FIG. 6.Alternatively, the three-dimensional model 1020 may be created using thecrisscross boustrophedonic scan with multiple passes, where each passincludes a structure-facing approach in which the camera is angledupward toward the structure, as illustrated in FIG. 3 and FIG. 5.

The three-dimensional model 1020 may be displayed 1000 on an operator'selectronic computing device for display to an owner or anotherinterested individual. The three-dimensional model 1020 may be used toexplain the extent of damage and or as evidence of the accuracy of theassessment. For example, patch scans 1048 of a predetermined size,shape, location, etc. that conform to one or more industryspecifications may be displayed on the roof of the three-dimensionalmodel 1020. Damage marks and/or patch scan boundaries may be shown byannotating actual images. Alternatively, a digitally rendered model ofthe entire structure and/or roof may be developed that includes thedamage marks and/or patch scan boundaries. In various embodiments,tapping, mousing-over, or otherwise selecting a portion of the roof maydisplay the damage marks more clearly, such as with highlighting orcolor-coded effects based on severity or overall damage status of apatch scan region. Similarly, patch scan boundaries may be selectivelydisplayed as overlays with color coding to show damage severity.

In some embodiments, the UAV itself does the image processing togenerate the patch scan regions with identified damage points,associated boundaries, and overlay markings. In other embodiments, theUAV may upload the scan data to a cloud-based analysis system and/or tothe operator's electronic computing device for processing. Thus, in someembodiments, the displayed three-dimensional model 1020 may includeactual images of the rooftop and, when a region is selected, a patchscan region with a predefined size and/or shape is overlaid on the imageand damage marks within the overlaid scan patch are identified. In someembodiments, the system may perform an objective analysis of each damagemark to determine the extent of the damage. Color-coded annotations maybe used to visually illustrate the extent of the damage.

As a specific example, a patch scan region that shows little or nodamage may be outlined in green. A patch scan region with medium damagemay be outlined in yellow, while a heavily damaged region may beoutlined in red. Similar codings may be used for each individual damagepoints. Damage assessments may conform to industry practices andstandards for a given applicable administrative body.

FIG. 11 illustrates a close-up view of a patch scan analysis on anelectronic computing device, according to one embodiment. An operatormay select a patch scan region in the three-dimensional visualizationshown in FIG. 10. In response to the selection, the damage visualizationsoftware may present a zoomed in view of the patch scan region outlinedwith black lines in the corners. Each damage mark may be marked andannotated based on objective severity. Color coding may be used.Information regarding the sample size, location, and severity may betextually communicated as well.

FIG. 12 illustrates close-up views of patch scan analyses 1210, 1220,1230, and 1240 for each face of a roof on an electronic computingdevice, according to one embodiment. As per the illustrated example,each patch scan region 1210-1240 may be outlined with black lines in thecorners and damage marks highlighted and/or color-coded based onseverity. Textual information for each patch scan region 1210-1240 maydescribe the number of severe damage locations, the number of moderatedamage locations, and the number of minor damage locations. The damagevisualization software may also compute and/or display an overall damageseverity.

FIG. 13 illustrates a three-dimensional rendering of a house 920displayed on an operator's computing device with annotated damagemarkers and patch analysis locations 1345 on the roof 1321 thereof. Thedisplayed rending may include other objects, such as a tree 1322 andboundary markers 1350 to provide contextual information forunderstanding the images and damage illustrated.

FIG. 14 illustrates a UAV roof analysis system using the date and time1410 to identify and/or optionally eliminate shadows in image captures.As shown a UAV 1475 may receive the current date and time 1410. The UAV1475 may determine a shadow 1445 of obstacles 1422 on a site 1450. TheUAV 1475 may refrain from taking images of the portion of a roof 920covered by the shadow 1445 of the obstacle 1422, annotate or otherwiseidentify shadow 1445, and/or take additional images at a subsequent timewhen the shadow 1445 has moved. Further, the UAV 1475 may determine atime when the shadow 1445 will move away from the roof 1420. The UAVroof analysis system using the date may also adjust the camera angle onthe UAV 1475 to avoid shadows 1446 from the UAV 1475.

FIG. 15 illustrates a roof-type analysis result displaying a detectedshingle type as being “asphalt shingles” along with an image. An agentor homeowner can verify the accuracy in some embodiments.

FIG. 16 illustrates an estimate of repairs based on patch analyses and aroof-type analysis presented on an electronic computing device,according to various embodiments. In the illustrated embodiment, theroof is identified as being constructed as asphalt shingles. The totalsquare footage is measured by the UAV as being 1700 square feet.Thirty-three damage points are identified on four roof faces. Three ofthe four roof faces are noted as requiring replacement. In total, 1275square feet are recommended for replacement and 300 square feet arerecommended for repair. The average damage is considered moderate and acost per square foot for replacement is estimated at $7.00 per squarefoot for a total cost of just under $9,000.

According to various embodiments, a remediation determination system mayevaluate the severity of damage and the number of damage points on eachface of a roof and assign a remediation status thereto. For example,faces of the roof may be assigned a “replace” status, a “repair” status,or “no remediation is needed” status. The roof faces may be assigned aremediation status based on a patch region analysis with a patch have adefined dimension or based on a total number of damage points andseverity rankings on each face.

FIG. 17 illustrates a UAV roof analysis system for analyzing astructure, according to one embodiment. As illustrated, a user interface1710 may include a roof selection interface 1715 to receive anelectronic input from an operator or technician that identifies a roof tto be analyzed. The user interface 1710 may further include an interface1720 to receive user input identifying a desired roof analysis and/or toallow a user to specify the type of patch scans or crisscross flightpattern to conduct. For example, patch scan may be specified withspecific dimensions, shapes, sizes, etc. to conform to the requirementsor standards set by a governing entity or applicable standard.

The user interface 1710 may additionally or optionally include a hazardidentification interface 1725 allowing a user to identify one or morehazards proximate a structure or site identified using the roofselection interface 1715. A damage and estimate visualization interface1727 allows an operator to visualize and/or present to an interestedparty a visual representation of a damage assessment. Estimates forrepair and/or replacement (i.e. remediation) may also be prepared andpresented via the damage and estimate visualization interface 1727.

A control system 1730 may be onboard a UAV 1755, may be remote (e.g.,cloud-based), and/or integrated into the computing device running theuser interface 1710. The control system 1730 may provide instructions tothe UAV 1755 to cause it to conduct an assessment and roof analysis. Thecontrol system 1730 may include a camera control module 1735, othersensor control modules 1740, image and/or sensor processing modules1745, and/or scanning modules 1750 to implement boustrophedonic,crisscross, and/or patch scans. The UAV 1755 itself may include one ormore cameras 1760 that may be used simultaneously or successively and/ormay require manual swapping, one or more optical sensors 1765,ultrasonic sensors 1770, other sensors 1775, and one or more networkcommunication systems 1780. FIG. 17 is merely representative of oneexample embodiment, and numerous variations and combinations arepossible to implement the systems and methods described herein.

FIG. 18 illustrates a system for roof analysis including a library ofdata profiles 1889 for computer vision matching, according to oneembodiment. The UAV computer vision system 1800 may be onboard theaerial vehicle, cloud-based, or a combination thereof. The system 1800may include a processor 1830, memory 1840 and a network interface 1850connected to a computer-readable storage medium 1870 via a bus 1820.

A scanning module 1880 may incorporate or control any of the systemsdescribed herein and implement any of the methods described herein. Anavigation module 1882 may utilize navigation sensors of the UAV andinclude various control mechanisms for navigating the UAV to performscans, including boustrophedonic, loop, and/or micro scans with patchscan region analysis.

The risk zone generator 1884 may generate a risk zone associated withthe property (e.g., overhead power lines, vehicle, structure, tower,bridge, road, residence, commercial building, etc.) within which the UAVmay navigate while performing one or more types of scanning operations.The risk zone generator 1884 may tag portions of the risk zone withscan-relevant tags and obstacle tags to aid the scanning of the propertyand/or avoid obstacles during navigation.

During micro scans and patch scan analyses, a tag reading module 1886may receive information from tags based on the location of the UAVwithin the risk zone and relative to the property. The tag readingmodule 1886 may receive scan-relevant or navigation-relevantinformation. The information therein may be used to query a rule set1888. The rule set 1888 may modify a navigation pattern, flightdirection, scan type, scan details, or other action to be taken or beingtaken by the UAV in response to a rule set's interpretation ofinformation provided by a tag read by the tag reading module 1886.

The UAV computer vision system 1800 may also access a library of dataprofiles 1889. Scan data captured by the UAV of any type of sensor maybe compared and matched with data profiles within the library of dataprofiles 1889. In response to the UAV computer vision system 1800identifying a match within the library of data profiles 1889, the ruleset 1888 may dictate a modification to the scanning or navigationpattern. A flight pattern selection and control module may allow for theselection of a flight pattern and provide instructions (in real-time orvia an upload/download) for implementing a specific flight pattern andscan (e.g., in concert with scanning module 1880.

For example, a crisscross boustrophedonic flight pattern may beselected. An operator may select the number of boustrophedonic flightpatterns for the crisscross boustrophedonic flight patterns (e.g., 2, 3,4, . . . ) and/or the relative angles (e.g., 90 degrees, 120 degrees,22.5 degrees, etc.) of each of the boustrophedonic flight pattern in thecrisscross flight pattern. Furthermore, the flight pattern selection andcontrol module 1891 may allow for the selection of structure-facing endpasses in one or more the boustrophedonic flight patterns and,optionally, rounded structure-facing end passes in one or more of theboustrophedonic flight patterns.

FIG. 19 illustrates examples of possible library images of data profiles1905-1935, according to one embodiment. Many examples of data profilesmay not be optical and are not illustrated in the drawings. For example,infrared data profiles and ultrasound profiles may be used instead of orin addition to optical data profiles. For example, a false colorrepresentation of an infrared scan may be used to show water damage to aroof. The UAV system may capture sensor data and identify a material bycomparing the captured images with data profiles within a library ofdata profiles. For example, computer vision may be used to identify aroof as cedar shakes 1905, asphalt shingles 1910, or wood 1915.

Once a material is identified and scanning continues, subsequent imagescan be compared with other data profiles to identify defects or othercharacteristics. For example, windblown cedar shakes 1925 may beidentified through computer vision techniques. Hail pops in asphaltshingles 1430 may be identified by matching captured image data withstored data profiles. Similarly, defects in wood 1935 may be identifiedby matching captured sensor data with library data profiles.

This disclosure has been made with reference to various embodiments,including the best mode. However, those skilled in the art willrecognize that changes and modifications may be made to the embodimentswithout departing from the scope of the present disclosure. While theprinciples of this disclosure have been shown in various embodiments,many modifications of structure, arrangements, proportions, elements,materials, and components may be adapted for a specific environmentand/or operating requirements without departing from the principles andscope of this disclosure. These and other changes or modifications areintended to be included within the scope of the present disclosure.

This disclosure is to be regarded in an illustrative rather than arestrictive sense, and all such modifications are intended to beincluded within the scope thereof. Likewise, benefits, other advantages,and solutions to problems have been described above with regard tovarious embodiments. However, benefits, advantages, solutions toproblems, and any element(s) that may cause any benefit, advantage, orsolution to occur or become more pronounced are not to be construed as acritical, required, or essential feature or element.

What is claimed is:
 1. An unmanned autonomous vehicle (UAV) assessmentsystem for imaging a structure for three-dimensional model generation,comprising: at least one tilt-adjustable camera to capture images of astructure at multiple locations and at multiple angles during animplemented flight pattern; a flight pattern control system to cause theUAV to navigate a crisscross boustrophedonic flight pattern relative tothe structure that includes: a first boustrophedonic flight patterncomprising a first set of passes over the structure at a firstorientation during which the UAV is instructed to fly in a firstdirection with a first rotation and then in a second direction relativeto the first direction without rotating the UAV, and a secondboustrophedonic flight pattern comprising a second set of passes overthe structure at a second orientation during which the UAV is instructedto fly in a third direction at an angle relative to the first directionand with a second rotation that is different from the first rotation andthen in a fourth direction relative to the third direction withoutrotating the UAV; and an imaging system to: adjust a tilt angle of thecamera upward to capture a first structure-facing image of the structureduring an approach portion of each pass of the crisscrossboustrophedonic flight pattern, adjust the tilt angle of the cameradownward toward the earth to capture a downward-facing image of thestructure as the UAV passes over the structure during each pass of thecrisscross boustrophedonic flight pattern, and adjust the tilt angle ofthe camera upward toward the structure to capture a secondstructure-facing image of the structure during a withdraw portion ofeach pass of the crisscross boustrophedonic flight pattern.
 2. The UAVassessment system of claim 1, wherein the crisscross boustrophedonicflight pattern comprises a first boustrophedonic flight pattern at afirst elevation and a second boustrophedonic flight pattern at the same,first elevation.
 3. The UAV assessment system of claim 1, wherein atleast a portion of each pass of the first set of passes of the firstboustrophedonic flight pattern is parallel to a portion of each otherpass of the first set of passes of the first boustrophedonic flightpattern.
 4. The UAV assessment system of claim 1, wherein at least aportion of each pass of the second set of passes of the secondboustrophedonic flight pattern is parallel to a portion of each otherpass of the second set of passes of the second boustrophedonic flightpattern.
 5. The UAV assessment system of claim 1, wherein the firstorientation of the first set of passes of the first boustrophedonicflight pattern is at a substantially 90-degree angle relative to thesecond orientation of the second set of passes of the firstboustrophedonic flight pattern.
 6. The UAV assessment system of claim 1,wherein each pass of the boustrophedonic flight pattern is connected toa neighboring pass via a rounded pass-offset portion.
 7. The UAVassessment system of claim 1, wherein each pass of the first set ofpasses of the first boustrophedonic flight pattern is connected to aneighboring pass via a squared pass-offset portion.
 8. The UAVassessment system of claim 1, wherein each pass of the first set ofpasses of the first boustrophedonic flight pattern is connected to aneighboring pass via an angled pass-offset portion.
 9. The UAVassessment system of claim 1, wherein at least two of the passes of eachof the first and second sets of passes include a beginning portion andan end portion, the beginning portion offsetting the UAV relative to animmediately prior pass and the end portion being substantially parallelto at least a portion of the immediately prior pass.
 10. The UAVassessment system of claim 1, further comprising: a processing system togenerate a three-dimensional model of the structure using capturedstructure-facing images of the structure and captured downward-facingimages of the structure.
 11. An unmanned autonomous vehicle (UAV)assessment system for scanning at least a portion of a structure,comprising: a flight pattern control system to cause the UAV to navigatea crisscross boustrophedonic flight pattern relative to a structure; asensor to capture scan data of a structure during the navigation of eachboustrophedonic flight pattern of the crisscross boustrophedonic flightpattern; and a control system to: angle the sensor upward toward thestructure to capture structure-facing scan data during an approachportion of each pass of each of the boustrophedonic flight patterns thatincludes an approach portion, and angle the sensor downward toward theearth to capture downward-facing scan data during a flyover portion ofeach pass of each of the boustrophedonic flight patterns that includes aflyover portion.
 12. The UAV assessment system of claim 11, furthercomprising: a processing system to generate a three-dimensional mode ofthe structure using captured structure-facing images of the structureand captured downward-facing images of the structure.
 13. The UAVassessment system of claim 11, wherein the control system is furtherconfigured to angle the sensor backward toward the structure to capturestructure-facing scan data, without rotating the UAV, during a departureportion of each pass of each of the boustrophedonic flight patterns thatincludes a departure portion.
 14. The UAV assessment system of claim 13,further comprising: a processing system to generate a three-dimensionalmode of the structure using captured structure-facing images of thestructure and captured downward-facing images of the structure.
 15. TheUAV assessment system of claim 11, wherein the scan data comprisesinfrared images captured by an infrared light camera.
 16. An unmannedautonomous vehicle (UAV) assessment system for imaging a structure forthree-dimensional model generation, comprising: at least onetilt-adjustable camera to capture images of a structure at multiplelocations and at multiple angles during an implemented flight pattern; aflight pattern control system to cause the UAV to navigate a crisscrossboustrophedonic flight pattern relative to the structure in which atleast one portion of at least one pass of each of the boustrophedonicflight patterns in the crisscross boustrophedonic flight patterninstructs the UAV to fly in a first direction and then in a seconddirection opposite the first direction without rotating the UAV; and animaging system to: adjust a tilt angle of the camera upward to captureat least one structure-facing image of the structure during an approachportion of at least one pass of the crisscross boustrophedonic flightpattern, adjust the tilt angle of the camera downward toward the earthto capture at least one downward-facing image of the structure as theUAV passes over the structure during at least one pass of the crisscrossboustrophedonic flight pattern, and adjust the tilt angle of the cameraupward toward the structure to capture at least one structure-facingimage of the structure during a withdraw portion of at least one pass ofthe crisscross boustrophedonic flight pattern with the UAV flying in aninitial direction and then in a subsequent direction opposite theinitial direction without rotating the UAV.
 17. The UAV assessmentsystem of claim 16, further comprising: a processing system to generatea three-dimensional model of the structure using capturedstructure-facing images of the structure and captured downward-facingimages of the structure.